Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

# helper.download_extract('mnist', data_dir)
# helper.download_extract('celeba', data_dir)

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f37fb54a9b0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f37fb433fd0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
/home/debadyuti/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
  return f(*args, **kwds)
TensorFlow Version: 1.4.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    input_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], "input_real")
    input_z = tf.placeholder(tf.float32, [None, z_dim], "input_z")
    learning_rate = tf.placeholder(tf.float32, None, "learning_rate")

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha=0.01):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    
    def conv(inputs, filters, batch_norm=True):
        outputs = tf.layers.conv2d(inputs, filters, 5, 2, 'same')
        if batch_norm:
            outputs = tf.layers.batch_normalization(outputs, training=True)
        return leaky_relu(outputs)
        
    
    with tf.variable_scope("discriminator", reuse=reuse):
        # input 28*28*3
        x1 = conv(images, 64, batch_norm=False) # 14*14*64
        x2 = conv(x1, 128) # 7*7*128
        x3 = conv(x2, 256) # 4*4*256
        
        flat = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.01):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    with tf.variable_scope("generator", reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu(x1)
        # 7*7*512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, 1, 'SAME')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.layers.dropout(x2, training=is_train)
        x2 = leaky_relu(x2)
        # 7*7*256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, 2, 'SAME')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.layers.dropout(x3, training=is_train)
        x3 = leaky_relu(x3)
        # 14*14*128
    
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, 2, 'SAME')
        out = tf.tanh(logits)
        # 28*28*out_channel_dim
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.9):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * alpha))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    with tf.control_dependencies(update_ops):
        t_vars = tf.trainable_variables()
        
        d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
        g_vars = [var for var in t_vars if var.name.startswith('generator')]

        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.0004
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 2.5859... Generator Loss: 0.1917
Epoch 1/2... Batch 20... Discriminator Loss: 1.2228... Generator Loss: 0.7064
Epoch 1/2... Batch 30... Discriminator Loss: 0.6223... Generator Loss: 6.0937
Epoch 1/2... Batch 40... Discriminator Loss: 1.7759... Generator Loss: 6.9386
Epoch 1/2... Batch 50... Discriminator Loss: 0.6322... Generator Loss: 1.8458
Epoch 1/2... Batch 60... Discriminator Loss: 0.6960... Generator Loss: 3.2341
Epoch 1/2... Batch 70... Discriminator Loss: 2.2977... Generator Loss: 0.3575
Epoch 1/2... Batch 80... Discriminator Loss: 0.9375... Generator Loss: 0.9889
Epoch 1/2... Batch 90... Discriminator Loss: 1.3986... Generator Loss: 0.4786
Epoch 1/2... Batch 100... Discriminator Loss: 1.7849... Generator Loss: 0.4016
Epoch 1/2... Batch 110... Discriminator Loss: 1.0541... Generator Loss: 1.6034
Epoch 1/2... Batch 120... Discriminator Loss: 0.6982... Generator Loss: 1.8103
Epoch 1/2... Batch 130... Discriminator Loss: 0.7623... Generator Loss: 1.8548
Epoch 1/2... Batch 140... Discriminator Loss: 1.4224... Generator Loss: 0.5007
Epoch 1/2... Batch 150... Discriminator Loss: 0.8850... Generator Loss: 1.9683
Epoch 1/2... Batch 160... Discriminator Loss: 0.9592... Generator Loss: 2.1968
Epoch 1/2... Batch 170... Discriminator Loss: 0.9467... Generator Loss: 1.8488
Epoch 1/2... Batch 180... Discriminator Loss: 1.1608... Generator Loss: 0.6716
Epoch 1/2... Batch 190... Discriminator Loss: 1.0164... Generator Loss: 1.2502
Epoch 1/2... Batch 200... Discriminator Loss: 0.8437... Generator Loss: 1.1241
Epoch 1/2... Batch 210... Discriminator Loss: 1.0690... Generator Loss: 2.6633
Epoch 1/2... Batch 220... Discriminator Loss: 0.9056... Generator Loss: 1.0204
Epoch 1/2... Batch 230... Discriminator Loss: 1.1793... Generator Loss: 2.4200
Epoch 1/2... Batch 240... Discriminator Loss: 0.8870... Generator Loss: 1.3420
Epoch 1/2... Batch 250... Discriminator Loss: 1.5637... Generator Loss: 0.4035
Epoch 1/2... Batch 260... Discriminator Loss: 0.9781... Generator Loss: 2.4672
Epoch 1/2... Batch 270... Discriminator Loss: 1.1307... Generator Loss: 0.8359
Epoch 1/2... Batch 280... Discriminator Loss: 0.8034... Generator Loss: 1.6014
Epoch 1/2... Batch 290... Discriminator Loss: 1.3283... Generator Loss: 2.5839
Epoch 1/2... Batch 300... Discriminator Loss: 0.8545... Generator Loss: 1.4563
Epoch 1/2... Batch 310... Discriminator Loss: 0.8978... Generator Loss: 1.1715
Epoch 1/2... Batch 320... Discriminator Loss: 1.4895... Generator Loss: 0.5026
Epoch 1/2... Batch 330... Discriminator Loss: 0.9133... Generator Loss: 1.4572
Epoch 1/2... Batch 340... Discriminator Loss: 1.5941... Generator Loss: 0.3836
Epoch 1/2... Batch 350... Discriminator Loss: 0.9776... Generator Loss: 0.8448
Epoch 1/2... Batch 360... Discriminator Loss: 0.9171... Generator Loss: 1.0296
Epoch 1/2... Batch 370... Discriminator Loss: 1.2763... Generator Loss: 0.6916
Epoch 1/2... Batch 380... Discriminator Loss: 0.9117... Generator Loss: 1.0884
Epoch 1/2... Batch 390... Discriminator Loss: 1.6265... Generator Loss: 0.4163
Epoch 1/2... Batch 400... Discriminator Loss: 0.8586... Generator Loss: 1.9280
Epoch 1/2... Batch 410... Discriminator Loss: 1.1865... Generator Loss: 0.6179
Epoch 1/2... Batch 420... Discriminator Loss: 1.1526... Generator Loss: 0.7798
Epoch 1/2... Batch 430... Discriminator Loss: 1.8052... Generator Loss: 0.3756
Epoch 1/2... Batch 440... Discriminator Loss: 1.0964... Generator Loss: 0.8897
Epoch 1/2... Batch 450... Discriminator Loss: 1.0270... Generator Loss: 1.4588
Epoch 1/2... Batch 460... Discriminator Loss: 1.0824... Generator Loss: 0.8774
Epoch 1/2... Batch 470... Discriminator Loss: 1.1941... Generator Loss: 0.7243
Epoch 1/2... Batch 480... Discriminator Loss: 1.0918... Generator Loss: 0.7261
Epoch 1/2... Batch 490... Discriminator Loss: 0.9890... Generator Loss: 0.8772
Epoch 1/2... Batch 500... Discriminator Loss: 0.9379... Generator Loss: 0.9094
Epoch 1/2... Batch 510... Discriminator Loss: 1.5008... Generator Loss: 0.4779
Epoch 1/2... Batch 520... Discriminator Loss: 0.9348... Generator Loss: 0.8752
Epoch 1/2... Batch 530... Discriminator Loss: 1.3504... Generator Loss: 2.9275
Epoch 1/2... Batch 540... Discriminator Loss: 0.9639... Generator Loss: 1.8175
Epoch 1/2... Batch 550... Discriminator Loss: 0.9557... Generator Loss: 2.4162
Epoch 1/2... Batch 560... Discriminator Loss: 0.9995... Generator Loss: 0.9181
Epoch 1/2... Batch 570... Discriminator Loss: 0.6872... Generator Loss: 1.6785
Epoch 1/2... Batch 580... Discriminator Loss: 0.8580... Generator Loss: 1.1312
Epoch 1/2... Batch 590... Discriminator Loss: 0.8814... Generator Loss: 1.0423
Epoch 1/2... Batch 600... Discriminator Loss: 0.8343... Generator Loss: 1.9995
Epoch 1/2... Batch 610... Discriminator Loss: 0.7408... Generator Loss: 1.8276
Epoch 1/2... Batch 620... Discriminator Loss: 0.8906... Generator Loss: 1.1246
Epoch 1/2... Batch 630... Discriminator Loss: 1.0521... Generator Loss: 0.7858
Epoch 1/2... Batch 640... Discriminator Loss: 0.9287... Generator Loss: 2.5764
Epoch 1/2... Batch 650... Discriminator Loss: 0.9945... Generator Loss: 2.1716
Epoch 1/2... Batch 660... Discriminator Loss: 0.9443... Generator Loss: 0.8976
Epoch 1/2... Batch 670... Discriminator Loss: 0.7547... Generator Loss: 2.0014
Epoch 1/2... Batch 680... Discriminator Loss: 1.2564... Generator Loss: 0.6365
Epoch 1/2... Batch 690... Discriminator Loss: 0.7207... Generator Loss: 1.5757
Epoch 1/2... Batch 700... Discriminator Loss: 0.7968... Generator Loss: 1.8345
Epoch 1/2... Batch 710... Discriminator Loss: 0.8479... Generator Loss: 0.9777
Epoch 1/2... Batch 720... Discriminator Loss: 0.7805... Generator Loss: 1.3768
Epoch 1/2... Batch 730... Discriminator Loss: 0.6350... Generator Loss: 1.3845
Epoch 1/2... Batch 740... Discriminator Loss: 1.0325... Generator Loss: 1.5290
Epoch 1/2... Batch 750... Discriminator Loss: 1.1775... Generator Loss: 0.6234
Epoch 1/2... Batch 760... Discriminator Loss: 0.8035... Generator Loss: 1.3250
Epoch 1/2... Batch 770... Discriminator Loss: 0.7844... Generator Loss: 1.3453
Epoch 1/2... Batch 780... Discriminator Loss: 0.7558... Generator Loss: 1.5662
Epoch 1/2... Batch 790... Discriminator Loss: 0.7561... Generator Loss: 1.4008
Epoch 1/2... Batch 800... Discriminator Loss: 0.7390... Generator Loss: 1.6117
Epoch 1/2... Batch 810... Discriminator Loss: 0.7008... Generator Loss: 2.4022
Epoch 1/2... Batch 820... Discriminator Loss: 0.8472... Generator Loss: 2.0006
Epoch 1/2... Batch 830... Discriminator Loss: 0.6625... Generator Loss: 1.5493
Epoch 1/2... Batch 840... Discriminator Loss: 1.0149... Generator Loss: 2.8018
Epoch 1/2... Batch 850... Discriminator Loss: 0.7155... Generator Loss: 1.4287
Epoch 1/2... Batch 860... Discriminator Loss: 0.8960... Generator Loss: 0.8997
Epoch 1/2... Batch 870... Discriminator Loss: 0.7643... Generator Loss: 1.3524
Epoch 1/2... Batch 880... Discriminator Loss: 0.9024... Generator Loss: 1.6844
Epoch 1/2... Batch 890... Discriminator Loss: 0.9722... Generator Loss: 1.0041
Epoch 1/2... Batch 900... Discriminator Loss: 0.8489... Generator Loss: 2.5232
Epoch 1/2... Batch 910... Discriminator Loss: 0.9797... Generator Loss: 2.5256
Epoch 1/2... Batch 920... Discriminator Loss: 0.7778... Generator Loss: 2.3371
Epoch 1/2... Batch 930... Discriminator Loss: 1.6032... Generator Loss: 3.6093
Epoch 1/2... Batch 940... Discriminator Loss: 0.6023... Generator Loss: 2.2524
Epoch 1/2... Batch 950... Discriminator Loss: 0.7960... Generator Loss: 2.4625
Epoch 1/2... Batch 960... Discriminator Loss: 0.6968... Generator Loss: 1.4799
Epoch 1/2... Batch 970... Discriminator Loss: 1.7694... Generator Loss: 0.4791
Epoch 1/2... Batch 980... Discriminator Loss: 0.7610... Generator Loss: 1.2940
Epoch 1/2... Batch 990... Discriminator Loss: 0.6537... Generator Loss: 1.9485
Epoch 1/2... Batch 1000... Discriminator Loss: 0.5805... Generator Loss: 2.3481
Epoch 1/2... Batch 1010... Discriminator Loss: 0.7044... Generator Loss: 1.3187
Epoch 1/2... Batch 1020... Discriminator Loss: 0.9326... Generator Loss: 2.3734
Epoch 1/2... Batch 1030... Discriminator Loss: 0.9005... Generator Loss: 1.3226
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Epoch 2/2... Batch 1800... Discriminator Loss: 0.3984... Generator Loss: 3.6867
Epoch 2/2... Batch 1810... Discriminator Loss: 0.6900... Generator Loss: 1.9427
Epoch 2/2... Batch 1820... Discriminator Loss: 0.5305... Generator Loss: 3.6468
Epoch 2/2... Batch 1830... Discriminator Loss: 0.5520... Generator Loss: 2.3020
Epoch 2/2... Batch 1840... Discriminator Loss: 0.3720... Generator Loss: 3.5732
Epoch 2/2... Batch 1850... Discriminator Loss: 0.4087... Generator Loss: 3.2531
Epoch 2/2... Batch 1860... Discriminator Loss: 0.4001... Generator Loss: 4.1158
Epoch 2/2... Batch 1870... Discriminator Loss: 0.4103... Generator Loss: 3.0265

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0004
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 1.6905... Generator Loss: 7.0749
Epoch 1/1... Batch 20... Discriminator Loss: 1.3087... Generator Loss: 0.9691
Epoch 1/1... Batch 30... Discriminator Loss: 1.1622... Generator Loss: 9.1674
Epoch 1/1... Batch 40... Discriminator Loss: 1.0070... Generator Loss: 0.9538
Epoch 1/1... Batch 50... Discriminator Loss: 0.6669... Generator Loss: 1.7104
Epoch 1/1... Batch 60... Discriminator Loss: 0.4875... Generator Loss: 3.0039
Epoch 1/1... Batch 70... Discriminator Loss: 0.4493... Generator Loss: 2.7212
Epoch 1/1... Batch 80... Discriminator Loss: 0.6415... Generator Loss: 1.7638
Epoch 1/1... Batch 90... Discriminator Loss: 0.6002... Generator Loss: 1.6510
Epoch 1/1... Batch 100... Discriminator Loss: 0.6412... Generator Loss: 4.6149
Epoch 1/1... Batch 110... Discriminator Loss: 0.7103... Generator Loss: 1.8082
Epoch 1/1... Batch 120... Discriminator Loss: 1.7808... Generator Loss: 9.1477
Epoch 1/1... Batch 130... Discriminator Loss: 0.4647... Generator Loss: 2.8126
Epoch 1/1... Batch 140... Discriminator Loss: 0.4786... Generator Loss: 2.6299
Epoch 1/1... Batch 150... Discriminator Loss: 0.5148... Generator Loss: 4.2001
Epoch 1/1... Batch 160... Discriminator Loss: 0.5052... Generator Loss: 2.2717
Epoch 1/1... Batch 170... Discriminator Loss: 0.5051... Generator Loss: 2.1122
Epoch 1/1... Batch 180... Discriminator Loss: 0.5078... Generator Loss: 3.2025
Epoch 1/1... Batch 190... Discriminator Loss: 0.4616... Generator Loss: 2.5315
Epoch 1/1... Batch 200... Discriminator Loss: 0.7100... Generator Loss: 1.7742
Epoch 1/1... Batch 210... Discriminator Loss: 0.5459... Generator Loss: 3.1116
Epoch 1/1... Batch 220... Discriminator Loss: 0.5296... Generator Loss: 2.3062
Epoch 1/1... Batch 230... Discriminator Loss: 0.5573... Generator Loss: 1.9869
Epoch 1/1... Batch 240... Discriminator Loss: 0.5694... Generator Loss: 1.9516
Epoch 1/1... Batch 250... Discriminator Loss: 0.9183... Generator Loss: 0.9828
Epoch 1/1... Batch 260... Discriminator Loss: 1.2534... Generator Loss: 0.6734
Epoch 1/1... Batch 270... Discriminator Loss: 0.8710... Generator Loss: 3.8942
Epoch 1/1... Batch 280... Discriminator Loss: 0.6134... Generator Loss: 2.9087
Epoch 1/1... Batch 290... Discriminator Loss: 0.6093... Generator Loss: 1.8650
Epoch 1/1... Batch 300... Discriminator Loss: 1.0129... Generator Loss: 4.2387
Epoch 1/1... Batch 310... Discriminator Loss: 0.6283... Generator Loss: 1.9003
Epoch 1/1... Batch 320... Discriminator Loss: 1.1571... Generator Loss: 3.2210
Epoch 1/1... Batch 330... Discriminator Loss: 0.8713... Generator Loss: 2.9190
Epoch 1/1... Batch 340... Discriminator Loss: 0.8225... Generator Loss: 2.3701
Epoch 1/1... Batch 350... Discriminator Loss: 0.7125... Generator Loss: 1.3259
Epoch 1/1... Batch 360... Discriminator Loss: 0.9999... Generator Loss: 1.9531
Epoch 1/1... Batch 370... Discriminator Loss: 0.6909... Generator Loss: 1.4728
Epoch 1/1... Batch 380... Discriminator Loss: 0.8242... Generator Loss: 2.5252
Epoch 1/1... Batch 390... Discriminator Loss: 0.7763... Generator Loss: 1.4940
Epoch 1/1... Batch 400... Discriminator Loss: 1.6583... Generator Loss: 0.4360
Epoch 1/1... Batch 410... Discriminator Loss: 0.7987... Generator Loss: 1.3169
Epoch 1/1... Batch 420... Discriminator Loss: 0.7773... Generator Loss: 1.4368
Epoch 1/1... Batch 430... Discriminator Loss: 0.7315... Generator Loss: 2.3750
Epoch 1/1... Batch 440... Discriminator Loss: 0.9671... Generator Loss: 0.8825
Epoch 1/1... Batch 450... Discriminator Loss: 0.7698... Generator Loss: 1.8168
Epoch 1/1... Batch 460... Discriminator Loss: 0.9333... Generator Loss: 0.9651
Epoch 1/1... Batch 470... Discriminator Loss: 1.0807... Generator Loss: 0.7546
Epoch 1/1... Batch 480... Discriminator Loss: 0.6760... Generator Loss: 1.8659
Epoch 1/1... Batch 490... Discriminator Loss: 0.8378... Generator Loss: 1.5709
Epoch 1/1... Batch 500... Discriminator Loss: 1.2405... Generator Loss: 0.5544
Epoch 1/1... Batch 510... Discriminator Loss: 1.2565... Generator Loss: 0.5966
Epoch 1/1... Batch 520... Discriminator Loss: 1.2517... Generator Loss: 2.9755
Epoch 1/1... Batch 530... Discriminator Loss: 0.7078... Generator Loss: 1.4040
Epoch 1/1... Batch 540... Discriminator Loss: 1.5239... Generator Loss: 3.5358
Epoch 1/1... Batch 550... Discriminator Loss: 0.7381... Generator Loss: 1.4292
Epoch 1/1... Batch 560... Discriminator Loss: 1.2022... Generator Loss: 0.7577
Epoch 1/1... Batch 570... Discriminator Loss: 1.0515... Generator Loss: 0.9524
Epoch 1/1... Batch 580... Discriminator Loss: 1.2632... Generator Loss: 0.5813
Epoch 1/1... Batch 590... Discriminator Loss: 1.9007... Generator Loss: 0.3155
Epoch 1/1... Batch 600... Discriminator Loss: 1.8804... Generator Loss: 0.3127
Epoch 1/1... Batch 610... Discriminator Loss: 0.9127... Generator Loss: 1.1367
Epoch 1/1... Batch 620... Discriminator Loss: 1.1082... Generator Loss: 0.8034
Epoch 1/1... Batch 630... Discriminator Loss: 0.7894... Generator Loss: 2.2179
Epoch 1/1... Batch 640... Discriminator Loss: 0.9612... Generator Loss: 1.3206
Epoch 1/1... Batch 650... Discriminator Loss: 0.8889... Generator Loss: 1.3156
Epoch 1/1... Batch 660... Discriminator Loss: 1.3945... Generator Loss: 0.5059
Epoch 1/1... Batch 670... Discriminator Loss: 0.9474... Generator Loss: 2.1939
Epoch 1/1... Batch 680... Discriminator Loss: 1.0694... Generator Loss: 0.7037
Epoch 1/1... Batch 690... Discriminator Loss: 0.8052... Generator Loss: 1.5133
Epoch 1/1... Batch 700... Discriminator Loss: 2.1301... Generator Loss: 0.2351
Epoch 1/1... Batch 710... Discriminator Loss: 0.8844... Generator Loss: 1.0605
Epoch 1/1... Batch 720... Discriminator Loss: 0.9607... Generator Loss: 2.0550
Epoch 1/1... Batch 730... Discriminator Loss: 1.5170... Generator Loss: 0.5948
Epoch 1/1... Batch 740... Discriminator Loss: 1.0120... Generator Loss: 1.7392
Epoch 1/1... Batch 750... Discriminator Loss: 1.0235... Generator Loss: 0.7631
Epoch 1/1... Batch 760... Discriminator Loss: 0.6990... Generator Loss: 2.5618
Epoch 1/1... Batch 770... Discriminator Loss: 1.7816... Generator Loss: 3.2633
Epoch 1/1... Batch 780... Discriminator Loss: 1.3418... Generator Loss: 0.4767
Epoch 1/1... Batch 790... Discriminator Loss: 1.1560... Generator Loss: 0.8693
Epoch 1/1... Batch 800... Discriminator Loss: 0.9635... Generator Loss: 0.8929
Epoch 1/1... Batch 810... Discriminator Loss: 1.0123... Generator Loss: 1.5399
Epoch 1/1... Batch 820... Discriminator Loss: 1.2368... Generator Loss: 0.7388
Epoch 1/1... Batch 830... Discriminator Loss: 1.0383... Generator Loss: 2.5714
Epoch 1/1... Batch 840... Discriminator Loss: 0.9977... Generator Loss: 1.2323
Epoch 1/1... Batch 850... Discriminator Loss: 0.9392... Generator Loss: 1.3251
Epoch 1/1... Batch 860... Discriminator Loss: 0.8342... Generator Loss: 1.2101
Epoch 1/1... Batch 870... Discriminator Loss: 1.1168... Generator Loss: 0.8029
Epoch 1/1... Batch 880... Discriminator Loss: 0.9979... Generator Loss: 0.9104
Epoch 1/1... Batch 890... Discriminator Loss: 0.5398... Generator Loss: 2.0786
Epoch 1/1... Batch 900... Discriminator Loss: 0.9387... Generator Loss: 1.6868
Epoch 1/1... Batch 910... Discriminator Loss: 0.9344... Generator Loss: 1.9009
Epoch 1/1... Batch 920... Discriminator Loss: 1.0277... Generator Loss: 0.7962
Epoch 1/1... Batch 930... Discriminator Loss: 0.7815... Generator Loss: 1.2852
Epoch 1/1... Batch 940... Discriminator Loss: 1.2842... Generator Loss: 0.6288
Epoch 1/1... Batch 950... Discriminator Loss: 0.6091... Generator Loss: 2.2041
Epoch 1/1... Batch 960... Discriminator Loss: 1.5698... Generator Loss: 0.6230
Epoch 1/1... Batch 970... Discriminator Loss: 1.1306... Generator Loss: 0.7979
Epoch 1/1... Batch 980... Discriminator Loss: 0.9445... Generator Loss: 0.8796
Epoch 1/1... Batch 990... Discriminator Loss: 1.6347... Generator Loss: 0.4636
Epoch 1/1... Batch 1000... Discriminator Loss: 0.7632... Generator Loss: 1.4757
Epoch 1/1... Batch 1010... Discriminator Loss: 0.9723... Generator Loss: 1.4857
Epoch 1/1... Batch 1020... Discriminator Loss: 0.8491... Generator Loss: 1.2679
Epoch 1/1... Batch 1030... Discriminator Loss: 0.7362... Generator Loss: 1.3910
Epoch 1/1... Batch 1040... Discriminator Loss: 1.4613... Generator Loss: 0.5303
Epoch 1/1... Batch 1050... Discriminator Loss: 1.1519... Generator Loss: 0.7108
Epoch 1/1... Batch 1060... Discriminator Loss: 1.0244... Generator Loss: 0.8515
Epoch 1/1... Batch 1070... Discriminator Loss: 1.9087... Generator Loss: 0.2685
Epoch 1/1... Batch 1080... Discriminator Loss: 1.0501... Generator Loss: 0.7324
Epoch 1/1... Batch 1090... Discriminator Loss: 0.9656... Generator Loss: 1.1283
Epoch 1/1... Batch 1100... Discriminator Loss: 0.8391... Generator Loss: 1.3763
Epoch 1/1... Batch 1110... Discriminator Loss: 0.9017... Generator Loss: 1.6930
Epoch 1/1... Batch 1120... Discriminator Loss: 0.7967... Generator Loss: 1.2073
Epoch 1/1... Batch 1130... Discriminator Loss: 0.7513... Generator Loss: 1.5299
Epoch 1/1... Batch 1140... Discriminator Loss: 0.8932... Generator Loss: 1.0003
Epoch 1/1... Batch 1150... Discriminator Loss: 0.6243... Generator Loss: 1.7465
Epoch 1/1... Batch 1160... Discriminator Loss: 1.2221... Generator Loss: 0.6899
Epoch 1/1... Batch 1170... Discriminator Loss: 1.2588... Generator Loss: 1.7018
Epoch 1/1... Batch 1180... Discriminator Loss: 0.7276... Generator Loss: 1.3969
Epoch 1/1... Batch 1190... Discriminator Loss: 1.0643... Generator Loss: 2.2610
Epoch 1/1... Batch 1200... Discriminator Loss: 0.8538... Generator Loss: 1.1772
Epoch 1/1... Batch 1210... Discriminator Loss: 1.8732... Generator Loss: 0.4132
Epoch 1/1... Batch 1220... Discriminator Loss: 0.9320... Generator Loss: 1.1676
Epoch 1/1... Batch 1230... Discriminator Loss: 0.9452... Generator Loss: 1.3696
Epoch 1/1... Batch 1240... Discriminator Loss: 1.1999... Generator Loss: 0.7144
Epoch 1/1... Batch 1250... Discriminator Loss: 0.8818... Generator Loss: 1.0972
Epoch 1/1... Batch 1260... Discriminator Loss: 0.7781... Generator Loss: 1.6136
Epoch 1/1... Batch 1270... Discriminator Loss: 1.2589... Generator Loss: 0.5636
Epoch 1/1... Batch 1280... Discriminator Loss: 1.6060... Generator Loss: 0.4451
Epoch 1/1... Batch 1290... Discriminator Loss: 0.9990... Generator Loss: 1.4700
Epoch 1/1... Batch 1300... Discriminator Loss: 1.4151... Generator Loss: 0.6488
Epoch 1/1... Batch 1310... Discriminator Loss: 0.8643... Generator Loss: 1.2751
Epoch 1/1... Batch 1320... Discriminator Loss: 1.2493... Generator Loss: 0.7104
Epoch 1/1... Batch 1330... Discriminator Loss: 1.0402... Generator Loss: 0.8361
Epoch 1/1... Batch 1340... Discriminator Loss: 1.0891... Generator Loss: 2.3864
Epoch 1/1... Batch 1350... Discriminator Loss: 0.7946... Generator Loss: 1.5685
Epoch 1/1... Batch 1360... Discriminator Loss: 0.9620... Generator Loss: 1.0128
Epoch 1/1... Batch 1370... Discriminator Loss: 0.9367... Generator Loss: 1.3107
Epoch 1/1... Batch 1380... Discriminator Loss: 1.0375... Generator Loss: 0.8550
Epoch 1/1... Batch 1390... Discriminator Loss: 0.8809... Generator Loss: 2.6504
Epoch 1/1... Batch 1400... Discriminator Loss: 1.1739... Generator Loss: 0.7573
Epoch 1/1... Batch 1410... Discriminator Loss: 1.4252... Generator Loss: 0.6120
Epoch 1/1... Batch 1420... Discriminator Loss: 0.8551... Generator Loss: 1.0686
Epoch 1/1... Batch 1430... Discriminator Loss: 0.8246... Generator Loss: 1.9564
Epoch 1/1... Batch 1440... Discriminator Loss: 1.2797... Generator Loss: 0.6427
Epoch 1/1... Batch 1450... Discriminator Loss: 1.0601... Generator Loss: 0.8326
Epoch 1/1... Batch 1460... Discriminator Loss: 1.0748... Generator Loss: 2.0521
Epoch 1/1... Batch 1470... Discriminator Loss: 0.8115... Generator Loss: 1.4838
Epoch 1/1... Batch 1480... Discriminator Loss: 0.7036... Generator Loss: 2.0671
Epoch 1/1... Batch 1490... Discriminator Loss: 1.4352... Generator Loss: 0.6288
Epoch 1/1... Batch 1500... Discriminator Loss: 2.1587... Generator Loss: 0.2965
Epoch 1/1... Batch 1510... Discriminator Loss: 0.9448... Generator Loss: 1.3122
Epoch 1/1... Batch 1520... Discriminator Loss: 0.8077... Generator Loss: 1.3500
Epoch 1/1... Batch 1530... Discriminator Loss: 0.8903... Generator Loss: 1.7416
Epoch 1/1... Batch 1540... Discriminator Loss: 2.2371... Generator Loss: 4.0052
Epoch 1/1... Batch 1550... Discriminator Loss: 0.7961... Generator Loss: 1.7734
Epoch 1/1... Batch 1560... Discriminator Loss: 0.8313... Generator Loss: 1.4393
Epoch 1/1... Batch 1570... Discriminator Loss: 0.9001... Generator Loss: 1.9618
Epoch 1/1... Batch 1580... Discriminator Loss: 0.7393... Generator Loss: 1.7151
Epoch 1/1... Batch 1590... Discriminator Loss: 0.9590... Generator Loss: 0.8678
Epoch 1/1... Batch 1600... Discriminator Loss: 0.6384... Generator Loss: 1.7000
Epoch 1/1... Batch 1610... Discriminator Loss: 0.9562... Generator Loss: 1.0085
Epoch 1/1... Batch 1620... Discriminator Loss: 1.7649... Generator Loss: 3.1089
Epoch 1/1... Batch 1630... Discriminator Loss: 1.0206... Generator Loss: 0.9791
Epoch 1/1... Batch 1640... Discriminator Loss: 1.2096... Generator Loss: 0.6225
Epoch 1/1... Batch 1650... Discriminator Loss: 1.0643... Generator Loss: 0.8150
Epoch 1/1... Batch 1660... Discriminator Loss: 0.8831... Generator Loss: 1.4734
Epoch 1/1... Batch 1670... Discriminator Loss: 0.8245... Generator Loss: 1.3162
Epoch 1/1... Batch 1680... Discriminator Loss: 1.4110... Generator Loss: 0.5483
Epoch 1/1... Batch 1690... Discriminator Loss: 0.7309... Generator Loss: 1.0939
Epoch 1/1... Batch 1700... Discriminator Loss: 0.9210... Generator Loss: 1.9578
Epoch 1/1... Batch 1710... Discriminator Loss: 1.3005... Generator Loss: 0.7027
Epoch 1/1... Batch 1720... Discriminator Loss: 0.8355... Generator Loss: 1.2688
Epoch 1/1... Batch 1730... Discriminator Loss: 0.6608... Generator Loss: 1.6702
Epoch 1/1... Batch 1740... Discriminator Loss: 0.9318... Generator Loss: 1.0815
Epoch 1/1... Batch 1750... Discriminator Loss: 0.9635... Generator Loss: 0.9289
Epoch 1/1... Batch 1760... Discriminator Loss: 0.7446... Generator Loss: 1.4463
Epoch 1/1... Batch 1770... Discriminator Loss: 0.7333... Generator Loss: 1.8188
Epoch 1/1... Batch 1780... Discriminator Loss: 0.9751... Generator Loss: 3.4058
Epoch 1/1... Batch 1790... Discriminator Loss: 1.1976... Generator Loss: 2.3490
Epoch 1/1... Batch 1800... Discriminator Loss: 0.9324... Generator Loss: 1.8036
Epoch 1/1... Batch 1810... Discriminator Loss: 0.9740... Generator Loss: 1.6952
Epoch 1/1... Batch 1820... Discriminator Loss: 1.5093... Generator Loss: 0.4387
Epoch 1/1... Batch 1830... Discriminator Loss: 1.0534... Generator Loss: 0.7143
Epoch 1/1... Batch 1840... Discriminator Loss: 0.8794... Generator Loss: 1.1993
Epoch 1/1... Batch 1850... Discriminator Loss: 0.8101... Generator Loss: 1.9833
Epoch 1/1... Batch 1860... Discriminator Loss: 0.7995... Generator Loss: 1.4134
Epoch 1/1... Batch 1870... Discriminator Loss: 0.8968... Generator Loss: 0.9981
Epoch 1/1... Batch 1880... Discriminator Loss: 1.0217... Generator Loss: 1.3748
Epoch 1/1... Batch 1890... Discriminator Loss: 1.2388... Generator Loss: 0.6996
Epoch 1/1... Batch 1900... Discriminator Loss: 1.2364... Generator Loss: 0.7528
Epoch 1/1... Batch 1910... Discriminator Loss: 0.7696... Generator Loss: 1.4870
Epoch 1/1... Batch 1920... Discriminator Loss: 0.7347... Generator Loss: 1.5484
Epoch 1/1... Batch 1930... Discriminator Loss: 0.6913... Generator Loss: 1.9708
Epoch 1/1... Batch 1940... Discriminator Loss: 1.2614... Generator Loss: 2.4370
Epoch 1/1... Batch 1950... Discriminator Loss: 0.8032... Generator Loss: 1.8182
Epoch 1/1... Batch 1960... Discriminator Loss: 0.8060... Generator Loss: 1.3176
Epoch 1/1... Batch 1970... Discriminator Loss: 0.8792... Generator Loss: 3.0300
Epoch 1/1... Batch 1980... Discriminator Loss: 1.0817... Generator Loss: 0.8204
Epoch 1/1... Batch 1990... Discriminator Loss: 1.0060... Generator Loss: 0.8886
Epoch 1/1... Batch 2000... Discriminator Loss: 0.7669... Generator Loss: 1.4976
Epoch 1/1... Batch 2010... Discriminator Loss: 0.8084... Generator Loss: 1.4936
Epoch 1/1... Batch 2020... Discriminator Loss: 1.2955... Generator Loss: 0.6611
Epoch 1/1... Batch 2030... Discriminator Loss: 0.7468... Generator Loss: 1.2503
Epoch 1/1... Batch 2040... Discriminator Loss: 0.8113... Generator Loss: 1.6095
Epoch 1/1... Batch 2050... Discriminator Loss: 0.7313... Generator Loss: 1.3487
Epoch 1/1... Batch 2060... Discriminator Loss: 0.7011... Generator Loss: 2.7370
Epoch 1/1... Batch 2070... Discriminator Loss: 0.8161... Generator Loss: 1.6998
Epoch 1/1... Batch 2080... Discriminator Loss: 1.0168... Generator Loss: 0.9094
Epoch 1/1... Batch 2090... Discriminator Loss: 0.6609... Generator Loss: 1.4484
Epoch 1/1... Batch 2100... Discriminator Loss: 1.3428... Generator Loss: 0.5503
Epoch 1/1... Batch 2110... Discriminator Loss: 0.6987... Generator Loss: 1.4949
Epoch 1/1... Batch 2120... Discriminator Loss: 0.9356... Generator Loss: 0.9223
Epoch 1/1... Batch 2130... Discriminator Loss: 0.9080... Generator Loss: 1.1685
Epoch 1/1... Batch 2140... Discriminator Loss: 1.1408... Generator Loss: 0.7054
Epoch 1/1... Batch 2150... Discriminator Loss: 0.8075... Generator Loss: 1.2666
Epoch 1/1... Batch 2160... Discriminator Loss: 0.7971... Generator Loss: 1.5059
Epoch 1/1... Batch 2170... Discriminator Loss: 0.7543... Generator Loss: 1.6195
Epoch 1/1... Batch 2180... Discriminator Loss: 0.8699... Generator Loss: 2.8072
Epoch 1/1... Batch 2190... Discriminator Loss: 0.8974... Generator Loss: 0.9508
Epoch 1/1... Batch 2200... Discriminator Loss: 1.5754... Generator Loss: 0.4769
Epoch 1/1... Batch 2210... Discriminator Loss: 0.8754... Generator Loss: 1.3524
Epoch 1/1... Batch 2220... Discriminator Loss: 0.9331... Generator Loss: 0.9042
Epoch 1/1... Batch 2230... Discriminator Loss: 1.4066... Generator Loss: 0.4362
Epoch 1/1... Batch 2240... Discriminator Loss: 0.8816... Generator Loss: 2.6746
Epoch 1/1... Batch 2250... Discriminator Loss: 0.7019... Generator Loss: 2.5257
Epoch 1/1... Batch 2260... Discriminator Loss: 2.0212... Generator Loss: 4.1182
Epoch 1/1... Batch 2270... Discriminator Loss: 0.9496... Generator Loss: 1.3346
Epoch 1/1... Batch 2280... Discriminator Loss: 1.0110... Generator Loss: 0.9314
Epoch 1/1... Batch 2290... Discriminator Loss: 0.7293... Generator Loss: 1.5307
Epoch 1/1... Batch 2300... Discriminator Loss: 0.8250... Generator Loss: 1.1173
Epoch 1/1... Batch 2310... Discriminator Loss: 1.4430... Generator Loss: 0.3871
Epoch 1/1... Batch 2320... Discriminator Loss: 0.7827... Generator Loss: 1.6614
Epoch 1/1... Batch 2330... Discriminator Loss: 0.8182... Generator Loss: 2.3038
Epoch 1/1... Batch 2340... Discriminator Loss: 0.5925... Generator Loss: 1.9768
Epoch 1/1... Batch 2350... Discriminator Loss: 0.7663... Generator Loss: 1.6827
Epoch 1/1... Batch 2360... Discriminator Loss: 0.5903... Generator Loss: 2.1362
Epoch 1/1... Batch 2370... Discriminator Loss: 0.8622... Generator Loss: 1.3890
Epoch 1/1... Batch 2380... Discriminator Loss: 0.8429... Generator Loss: 1.6237
Epoch 1/1... Batch 2390... Discriminator Loss: 1.3138... Generator Loss: 0.6170
Epoch 1/1... Batch 2400... Discriminator Loss: 1.5893... Generator Loss: 0.4904
Epoch 1/1... Batch 2410... Discriminator Loss: 0.7863... Generator Loss: 3.9039
Epoch 1/1... Batch 2420... Discriminator Loss: 0.6575... Generator Loss: 1.5273
Epoch 1/1... Batch 2430... Discriminator Loss: 0.7680... Generator Loss: 2.1439
Epoch 1/1... Batch 2440... Discriminator Loss: 0.7141... Generator Loss: 2.4530
Epoch 1/1... Batch 2450... Discriminator Loss: 1.0183... Generator Loss: 0.8421
Epoch 1/1... Batch 2460... Discriminator Loss: 0.8744... Generator Loss: 2.6945
Epoch 1/1... Batch 2470... Discriminator Loss: 1.0333... Generator Loss: 3.0621
Epoch 1/1... Batch 2480... Discriminator Loss: 0.5374... Generator Loss: 2.4422
Epoch 1/1... Batch 2490... Discriminator Loss: 0.5707... Generator Loss: 1.7861
Epoch 1/1... Batch 2500... Discriminator Loss: 0.8363... Generator Loss: 1.1731
Epoch 1/1... Batch 2510... Discriminator Loss: 0.4809... Generator Loss: 2.4634
Epoch 1/1... Batch 2520... Discriminator Loss: 0.3693... Generator Loss: 3.5118
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Epoch 1/1... Batch 5260... Discriminator Loss: 0.4960... Generator Loss: 2.8378
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Epoch 1/1... Batch 5280... Discriminator Loss: 0.6658... Generator Loss: 2.0392
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Epoch 1/1... Batch 5310... Discriminator Loss: 0.4494... Generator Loss: 3.0235
Epoch 1/1... Batch 5320... Discriminator Loss: 0.4846... Generator Loss: 2.9374
Epoch 1/1... Batch 5330... Discriminator Loss: 0.7547... Generator Loss: 1.3628
Epoch 1/1... Batch 5340... Discriminator Loss: 0.6423... Generator Loss: 1.9489
Epoch 1/1... Batch 5350... Discriminator Loss: 0.4116... Generator Loss: 3.5829
Epoch 1/1... Batch 5360... Discriminator Loss: 0.3856... Generator Loss: 4.2615
Epoch 1/1... Batch 5370... Discriminator Loss: 0.3777... Generator Loss: 3.1034
Epoch 1/1... Batch 5380... Discriminator Loss: 0.4974... Generator Loss: 2.4937
Epoch 1/1... Batch 5390... Discriminator Loss: 1.0437... Generator Loss: 1.1843
Epoch 1/1... Batch 5400... Discriminator Loss: 0.8138... Generator Loss: 3.3122
Epoch 1/1... Batch 5410... Discriminator Loss: 0.8762... Generator Loss: 3.2337
Epoch 1/1... Batch 5420... Discriminator Loss: 0.4447... Generator Loss: 2.5970
Epoch 1/1... Batch 5430... Discriminator Loss: 0.4059... Generator Loss: 4.2987
Epoch 1/1... Batch 5440... Discriminator Loss: 0.3919... Generator Loss: 3.0580
Epoch 1/1... Batch 5450... Discriminator Loss: 1.0956... Generator Loss: 4.2791
Epoch 1/1... Batch 5460... Discriminator Loss: 0.4123... Generator Loss: 3.1299
Epoch 1/1... Batch 5470... Discriminator Loss: 0.6060... Generator Loss: 4.7068
Epoch 1/1... Batch 5480... Discriminator Loss: 0.4727... Generator Loss: 2.3138
Epoch 1/1... Batch 5490... Discriminator Loss: 0.4193... Generator Loss: 3.2560
Epoch 1/1... Batch 5500... Discriminator Loss: 0.4156... Generator Loss: 3.1945
Epoch 1/1... Batch 5510... Discriminator Loss: 1.8367... Generator Loss: 3.9731
Epoch 1/1... Batch 5520... Discriminator Loss: 0.4938... Generator Loss: 3.1560
Epoch 1/1... Batch 5530... Discriminator Loss: 0.6028... Generator Loss: 1.8958
Epoch 1/1... Batch 5540... Discriminator Loss: 1.3496... Generator Loss: 1.0090
Epoch 1/1... Batch 5550... Discriminator Loss: 0.8863... Generator Loss: 1.2687
Epoch 1/1... Batch 5560... Discriminator Loss: 0.4896... Generator Loss: 2.3522
Epoch 1/1... Batch 5570... Discriminator Loss: 1.4569... Generator Loss: 0.5183
Epoch 1/1... Batch 5580... Discriminator Loss: 0.3748... Generator Loss: 3.8072
Epoch 1/1... Batch 5590... Discriminator Loss: 0.4448... Generator Loss: 2.8542
Epoch 1/1... Batch 5600... Discriminator Loss: 0.3994... Generator Loss: 4.2163
Epoch 1/1... Batch 5610... Discriminator Loss: 0.4855... Generator Loss: 2.1654
Epoch 1/1... Batch 5620... Discriminator Loss: 1.3402... Generator Loss: 3.1868
Epoch 1/1... Batch 5630... Discriminator Loss: 0.7496... Generator Loss: 1.2897
Epoch 1/1... Batch 5640... Discriminator Loss: 0.4886... Generator Loss: 2.8016
Epoch 1/1... Batch 5650... Discriminator Loss: 0.8038... Generator Loss: 3.5418
Epoch 1/1... Batch 5660... Discriminator Loss: 0.8049... Generator Loss: 1.2582
Epoch 1/1... Batch 5670... Discriminator Loss: 0.4777... Generator Loss: 2.7110
Epoch 1/1... Batch 5680... Discriminator Loss: 0.9602... Generator Loss: 0.9376
Epoch 1/1... Batch 5690... Discriminator Loss: 0.4570... Generator Loss: 3.3955
Epoch 1/1... Batch 5700... Discriminator Loss: 0.7874... Generator Loss: 1.4797
Epoch 1/1... Batch 5710... Discriminator Loss: 0.5120... Generator Loss: 2.3765
Epoch 1/1... Batch 5720... Discriminator Loss: 0.5342... Generator Loss: 3.7731
Epoch 1/1... Batch 5730... Discriminator Loss: 0.7621... Generator Loss: 5.3117
Epoch 1/1... Batch 5740... Discriminator Loss: 0.6485... Generator Loss: 1.9208
Epoch 1/1... Batch 5750... Discriminator Loss: 1.3510... Generator Loss: 1.8908
Epoch 1/1... Batch 5760... Discriminator Loss: 0.6184... Generator Loss: 2.1753
Epoch 1/1... Batch 5770... Discriminator Loss: 0.4851... Generator Loss: 2.3596
Epoch 1/1... Batch 5780... Discriminator Loss: 0.4191... Generator Loss: 3.3803
Epoch 1/1... Batch 5790... Discriminator Loss: 0.3894... Generator Loss: 4.3245
Epoch 1/1... Batch 5800... Discriminator Loss: 0.5171... Generator Loss: 2.3499
Epoch 1/1... Batch 5810... Discriminator Loss: 1.6170... Generator Loss: 0.5510
Epoch 1/1... Batch 5820... Discriminator Loss: 0.4587... Generator Loss: 3.1688
Epoch 1/1... Batch 5830... Discriminator Loss: 0.4334... Generator Loss: 2.4766
Epoch 1/1... Batch 5840... Discriminator Loss: 0.5327... Generator Loss: 2.1532
Epoch 1/1... Batch 5850... Discriminator Loss: 0.4621... Generator Loss: 4.5367
Epoch 1/1... Batch 5860... Discriminator Loss: 0.6253... Generator Loss: 1.7440
Epoch 1/1... Batch 5870... Discriminator Loss: 0.5164... Generator Loss: 2.6522
Epoch 1/1... Batch 5880... Discriminator Loss: 0.6474... Generator Loss: 2.7764
Epoch 1/1... Batch 5890... Discriminator Loss: 0.5950... Generator Loss: 4.9739
Epoch 1/1... Batch 5900... Discriminator Loss: 0.9044... Generator Loss: 2.8039
Epoch 1/1... Batch 5910... Discriminator Loss: 0.4494... Generator Loss: 2.9943
Epoch 1/1... Batch 5920... Discriminator Loss: 0.4502... Generator Loss: 4.0557
Epoch 1/1... Batch 5930... Discriminator Loss: 1.0769... Generator Loss: 3.8610
Epoch 1/1... Batch 5940... Discriminator Loss: 0.6904... Generator Loss: 1.2953
Epoch 1/1... Batch 5950... Discriminator Loss: 0.4963... Generator Loss: 2.3455
Epoch 1/1... Batch 5960... Discriminator Loss: 0.6638... Generator Loss: 1.8028
Epoch 1/1... Batch 5970... Discriminator Loss: 0.6932... Generator Loss: 1.6814
Epoch 1/1... Batch 5980... Discriminator Loss: 0.4759... Generator Loss: 2.4261
Epoch 1/1... Batch 5990... Discriminator Loss: 0.4065... Generator Loss: 3.1134
Epoch 1/1... Batch 6000... Discriminator Loss: 0.7163... Generator Loss: 1.4002
Epoch 1/1... Batch 6010... Discriminator Loss: 0.6113... Generator Loss: 1.9609
Epoch 1/1... Batch 6020... Discriminator Loss: 0.9673... Generator Loss: 2.6335
Epoch 1/1... Batch 6030... Discriminator Loss: 1.0736... Generator Loss: 3.6542
Epoch 1/1... Batch 6040... Discriminator Loss: 1.1889... Generator Loss: 0.9660
Epoch 1/1... Batch 6050... Discriminator Loss: 1.1545... Generator Loss: 1.0046
Epoch 1/1... Batch 6060... Discriminator Loss: 0.5635... Generator Loss: 2.1634
Epoch 1/1... Batch 6070... Discriminator Loss: 0.4560... Generator Loss: 2.5459
Epoch 1/1... Batch 6080... Discriminator Loss: 0.9402... Generator Loss: 1.4470
Epoch 1/1... Batch 6090... Discriminator Loss: 0.5767... Generator Loss: 2.2867
Epoch 1/1... Batch 6100... Discriminator Loss: 0.3700... Generator Loss: 4.4826
Epoch 1/1... Batch 6110... Discriminator Loss: 0.8259... Generator Loss: 1.2931
Epoch 1/1... Batch 6120... Discriminator Loss: 0.7103... Generator Loss: 1.2186
Epoch 1/1... Batch 6130... Discriminator Loss: 0.5060... Generator Loss: 2.4408
Epoch 1/1... Batch 6140... Discriminator Loss: 0.4580... Generator Loss: 3.1708
Epoch 1/1... Batch 6150... Discriminator Loss: 0.4885... Generator Loss: 4.4281
Epoch 1/1... Batch 6160... Discriminator Loss: 0.7096... Generator Loss: 1.6591
Epoch 1/1... Batch 6170... Discriminator Loss: 0.5249... Generator Loss: 2.9614
Epoch 1/1... Batch 6180... Discriminator Loss: 0.8281... Generator Loss: 1.9576
Epoch 1/1... Batch 6190... Discriminator Loss: 0.6774... Generator Loss: 1.6093
Epoch 1/1... Batch 6200... Discriminator Loss: 0.3911... Generator Loss: 3.5425
Epoch 1/1... Batch 6210... Discriminator Loss: 0.6043... Generator Loss: 1.6659
Epoch 1/1... Batch 6220... Discriminator Loss: 0.4271... Generator Loss: 2.4263
Epoch 1/1... Batch 6230... Discriminator Loss: 0.5206... Generator Loss: 4.5862
Epoch 1/1... Batch 6240... Discriminator Loss: 0.5660... Generator Loss: 1.9315
Epoch 1/1... Batch 6250... Discriminator Loss: 0.4203... Generator Loss: 3.6837
Epoch 1/1... Batch 6260... Discriminator Loss: 0.7410... Generator Loss: 1.6608
Epoch 1/1... Batch 6270... Discriminator Loss: 1.2528... Generator Loss: 1.2574
Epoch 1/1... Batch 6280... Discriminator Loss: 0.5726... Generator Loss: 2.3963
Epoch 1/1... Batch 6290... Discriminator Loss: 0.8423... Generator Loss: 1.0037
Epoch 1/1... Batch 6300... Discriminator Loss: 0.4443... Generator Loss: 2.7699
Epoch 1/1... Batch 6310... Discriminator Loss: 0.4346... Generator Loss: 3.1094
Epoch 1/1... Batch 6320... Discriminator Loss: 0.5759... Generator Loss: 1.4075
Epoch 1/1... Batch 6330... Discriminator Loss: 0.4687... Generator Loss: 2.5564

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.